Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System
journal contributionposted on 28.03.2022, 15:31 by Aihua Zhang, Pengcheng Liu, Bing Ning, Qiyu Zhou
This paper studies the issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme. A new sparsity adaptive system identification method is proposed, namely reweighted 𝒍𝒑 norm (𝟎 < 𝒑 < 𝟏) penalized least mean square（LMS）algorithm. The main idea of the algorithm is to add a 𝒍𝒑 norm penalty of sparsity into the cost function of the LMS algorithm. By doing so, the weight factor becomes a balance parameter of the associated 𝒍𝒑 norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted 𝒍𝒑 norm. With the upper bounds, we prove that the 𝒍𝒑 (𝟎 < 𝒑 < 𝟏 ) norm sparsity inducing cost function is superior to the reweighted 𝒍𝟏 norm. An optimal selection of 𝒑 for the 𝒍𝒑 norm problem is studied to recover various 𝒅 sparse channel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady state behavior than other LMS algorithms.
Published inIET Communications
PublisherInstitution of Engineering and Technology
VersionAM (Accepted Manuscript)
CitationZhang, A., Liu, P., Ning, B. and Zhou, Q. (2019) 'Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System'. DOI: 10.1049/iet-com.2018.6186.
Cardiff Met Affiliation
- Cardiff School of Technologies